CIND 820 :- Stroke Prediction project

Table of Contents

• Introduction
• Data Exploration
• Exploratory Data Analysis
• Data Processing & Modeling
• Conclusions

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Introduction

• The dataset to be used for this project are Stroke Prediction Dataset.
• https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset
• The dataset has about 5k records.
• There are 12 columns: -id - gender - age - hypertension -heart_disease - ever married - work_type - Residence_type - avg_glucose_level - bmi -smoking_status – stroke.

Importing Libaries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Data Exploration

df = pd.read_csv('healthcare-dataset-stroke-data.csv')
df.head()

	id	gender	age	hypertension	heart_disease	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
0	9046	Male	67.0	0	1	Yes	Private	Urban	228.69	36.6	formerly smoked	1
1	51676	Female	61.0	0	0	Yes	Self-employed	Rural	202.21	NaN	never smoked	1
2	31112	Male	80.0	0	1	Yes	Private	Rural	105.92	32.5	never smoked	1
3	60182	Female	49.0	0	0	Yes	Private	Urban	171.23	34.4	smokes	1
4	1665	Female	79.0	1	0	Yes	Self-employed	Rural	174.12	24.0	never smoked	1

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5110 entries, 0 to 5109
Data columns (total 12 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   id                 5110 non-null   int64  
 1   gender             5110 non-null   object 
 2   age                5110 non-null   float64
 3   hypertension       5110 non-null   int64  
 4   heart_disease      5110 non-null   int64  
 5   ever_married       5110 non-null   object 
 6   work_type          5110 non-null   object 
 7   Residence_type     5110 non-null   object 
 8   avg_glucose_level  5110 non-null   float64
 9   bmi                4909 non-null   float64
 10  smoking_status     5110 non-null   object 
 11  stroke             5110 non-null   int64  
dtypes: float64(3), int64(4), object(5)
memory usage: 479.2+ KB

df.describe()

	id	age	hypertension	heart_disease	avg_glucose_level	bmi	stroke
count	5110.000000	5110.000000	5110.000000	5110.000000	5110.000000	4909.000000	5110.000000
mean	36517.829354	43.226614	0.097456	0.054012	106.147677	28.893237	0.048728
std	21161.721625	22.612647	0.296607	0.226063	45.283560	7.854067	0.215320
min	67.000000	0.080000	0.000000	0.000000	55.120000	10.300000	0.000000
25%	17741.250000	25.000000	0.000000	0.000000	77.245000	23.500000	0.000000
50%	36932.000000	45.000000	0.000000	0.000000	91.885000	28.100000	0.000000
75%	54682.000000	61.000000	0.000000	0.000000	114.090000	33.100000	0.000000
max	72940.000000	82.000000	1.000000	1.000000	271.740000	97.600000	1.000000

df.isnull().sum()

id                     0
gender                 0
age                    0
hypertension           0
heart_disease          0
ever_married           0
work_type              0
Residence_type         0
avg_glucose_level      0
bmi                  201
smoking_status         0
stroke                 0
dtype: int64

df['stroke'].value_counts()

0    4861
1     249
Name: stroke, dtype: int64

this dataset was highly unbalanced. Only 249 patients suffered a stroke while the remaining 4861 patients did not have the experience.

# Drop the id column
df.drop(columns=['id'], inplace=True)

This attribute has no other useful information and was only used to identify patients.

# Showing records where patient suffered from stroke but had missing value in bmi attribute.
df[df['bmi'].isna() & df['stroke'] == 1]

	gender	age	hypertension	heart_disease	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
1	Female	61.00	0	0	Yes	Self-employed	Rural	202.21	NaN	never smoked	1
8	Female	59.00	0	0	Yes	Private	Rural	76.15	NaN	Unknown	1
13	Male	78.00	0	1	Yes	Private	Urban	219.84	NaN	Unknown	1
19	Male	57.00	0	1	No	Govt_job	Urban	217.08	NaN	Unknown	1
27	Male	58.00	0	0	Yes	Private	Rural	189.84	NaN	Unknown	1
29	Male	59.00	0	0	Yes	Private	Rural	211.78	NaN	formerly smoked	1
43	Female	63.00	0	0	Yes	Private	Urban	90.90	NaN	formerly smoked	1
46	Female	75.00	0	1	No	Self-employed	Urban	109.78	NaN	Unknown	1
50	Female	76.00	0	0	No	Private	Urban	89.96	NaN	Unknown	1
51	Male	78.00	1	0	Yes	Private	Urban	75.32	NaN	formerly smoked	1
54	Female	63.00	0	0	Yes	Govt_job	Urban	197.54	NaN	never smoked	1
57	Male	78.00	0	0	Yes	Private	Urban	237.75	NaN	formerly smoked	1
64	Male	75.00	0	0	Yes	Private	Urban	104.72	NaN	Unknown	1
70	Female	76.00	0	0	Yes	Govt_job	Rural	62.57	NaN	formerly smoked	1
78	Female	51.00	0	0	Yes	Private	Urban	165.31	NaN	never smoked	1
81	Female	66.00	0	0	Yes	Self-employed	Urban	101.45	NaN	Unknown	1
84	Male	58.00	0	0	Yes	Private	Urban	71.20	NaN	Unknown	1
105	Male	58.00	0	0	Yes	Private	Urban	82.30	NaN	smokes	1
112	Female	76.00	0	0	Yes	Self-employed	Urban	106.41	NaN	formerly smoked	1
124	Female	72.00	0	0	Yes	Private	Urban	219.91	NaN	Unknown	1
126	Male	78.00	1	0	Yes	Self-employed	Urban	93.13	NaN	formerly smoked	1
129	Female	75.00	0	0	Yes	Govt_job	Urban	62.48	NaN	Unknown	1
133	Female	38.00	0	0	Yes	Private	Rural	101.45	NaN	formerly smoked	1
146	Male	65.00	0	0	Yes	Self-employed	Urban	68.43	NaN	formerly smoked	1
150	Female	79.00	0	0	Yes	Private	Rural	169.67	NaN	Unknown	1
160	Female	76.00	0	0	Yes	Private	Urban	57.92	NaN	formerly smoked	1
161	Male	71.00	0	1	Yes	Private	Urban	81.76	NaN	smokes	1
162	Female	1.32	0	0	No	children	Urban	70.37	NaN	Unknown	1
167	Male	79.00	1	0	Yes	Private	Rural	75.02	NaN	never smoked	1
170	Male	64.00	0	0	Yes	Self-employed	Rural	111.98	NaN	formerly smoked	1
171	Female	79.00	1	1	No	Self-employed	Rural	60.94	NaN	never smoked	1
174	Female	78.00	0	0	Yes	Self-employed	Rural	60.67	NaN	formerly smoked	1
178	Female	80.00	0	0	Yes	Govt_job	Urban	110.66	NaN	Unknown	1
183	Female	77.00	0	0	No	Private	Urban	81.32	NaN	Unknown	1
189	Male	61.00	0	1	Yes	Private	Urban	209.86	NaN	Unknown	1
198	Male	79.00	0	0	Yes	Private	Rural	114.77	NaN	formerly smoked	1
200	Male	74.00	0	0	Yes	Private	Urban	167.13	NaN	Unknown	1
218	Female	76.00	1	1	Yes	Self-employed	Urban	199.86	NaN	smokes	1
227	Male	74.00	0	0	Yes	Self-employed	Rural	60.98	NaN	never smoked	1
247	Male	71.00	1	0	Yes	Self-employed	Rural	87.80	NaN	Unknown	1

# Replace the missing values with mean of bmi attribute
df['bmi'].fillna(np.round(df['bmi'].mean(), 1), inplace = True)

records with empty value in BMI was replaced with mean of BMI.

df = df[df['gender'] != 'Other']

only one record were categorized as ‘Other’, so it was deleted

Gender needs to be categorized as binary variable.

Exploratory Data Analysis

Univariate Analysis

numerical = ['age','avg_glucose_level', 'bmi']
df[numerical].hist(figsize=(8,6));

• As seen in both skewness result and histograms, numerical features have skewness in different degrees.

Categorical Features

sns.countplot(data=df,x='gender')

<AxesSubplot:xlabel='gender', ylabel='count'>

We have 2994 female and 2115 male

sns.countplot(data=df,x='hypertension')

<AxesSubplot:xlabel='hypertension', ylabel='count'>

• We have 498 patient with hypertension which represents at raound 10 % of the sample.

sns.countplot(data=df,x='heart_disease')

<AxesSubplot:xlabel='heart_disease', ylabel='count'>

• We have 276 patient with heart disease which is 5.4 % of the sample.

sns.countplot(data=df,x='ever_married')

<AxesSubplot:xlabel='ever_married', ylabel='count'>

• 3353 people have been married and 1757 people are not married before.

sns.countplot(data=df,x='work_type')

<AxesSubplot:xlabel='work_type', ylabel='count'>

• 2925 people work in the private sector.
• 819 people are self-employed
• 657 people work at the government job.

sns.countplot(data=df,x='smoking_status')

<AxesSubplot:xlabel='smoking_status', ylabel='count'>

• 1892 people are never smoked , 789 people smoke

Bivariate Analysis

# Gender & Stroke
sns.countplot(data=df,x='gender',hue='stroke')

<AxesSubplot:xlabel='gender', ylabel='count'>

there is not much difference between stroke rate concerning gender

# Hypertension & Stroke
sns.countplot(data=df,x='hypertension',hue='stroke')

<AxesSubplot:xlabel='hypertension', ylabel='count'>

Well, young people rarely have hypertension, while the elderly frequently do. A stroke can be brought on by hypertension. Our data do not paint a very clear picture of hypertension. On patients with hypertension, there is not a lot of information.

# Married & Stroke
sns.countplot(data=df,x='ever_married',hue='stroke')

<AxesSubplot:xlabel='ever_married', ylabel='count'>

People who are married have a higher stroke rate.

# Work Type & Stroke
sns.countplot(data=df,x='work_type',hue='stroke')

<AxesSubplot:xlabel='work_type', ylabel='count'>

People working in the Private sector have a higher risk of getting a stroke. And people who have never worked have a very less stroke rate.

# Work Type & Stroke
sns.countplot(data=df,x='Residence_type',hue='stroke')

<AxesSubplot:xlabel='Residence_type', ylabel='count'>

there not much difference in both attribute values. Maybe we have to discard it.

# Smoking & Stroke
sns.countplot(data=df,x='smoking_status',hue='stroke')

<AxesSubplot:xlabel='smoking_status', ylabel='count'>

• It is smal difference between who smokes and who does not smoke in regard to probability of getting stroke.

sns.boxplot(data=df,x='stroke',y='age')

<AxesSubplot:xlabel='stroke', ylabel='age'>

Strokes are more likely to occur in people over 60.

Strokes are more likely to occur in people over 60. Some anomalies can be identified as strokes occurring in people under the age of 20. Given that our food and living habits have an impact on stroke, it's probable that the data is accurate. Another finding is that those over 60 years old make up the group of people who do not experience strokes.

sns.boxplot(data=df,x='stroke',y='avg_glucose_level')

<AxesSubplot:xlabel='stroke', ylabel='avg_glucose_level'>

people having stroke have an average glucose level of more than 100.

sns.boxplot(data=df,x='stroke',y='bmi')

<AxesSubplot:xlabel='stroke', ylabel='bmi'>

The relationship between BMI and the risk of stroke has not yet been clearly observed.

sns.countplot(data=df,x='smoking_status',hue='stroke')

<AxesSubplot:xlabel='smoking_status', ylabel='count'>

there is not much difference in the chances of stroke irrespective of smoking status.

Since correlation check only accept numerical variables, preprocessing the categorical variables becomes a necessary step, we need to convert these categorical variables to numbers encoded to 0 or 1. We use labelEncoder from sklearn.preprocessing as it will be easy to decode a particular label back later after predicting if required.

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()

df['gender'] = le.fit_transform(df['gender'])
df['ever_married'] = le.fit_transform(df['ever_married'])
df['work_type'] = le.fit_transform(df['work_type'])
df['Residence_type'] = le.fit_transform(df['Residence_type'])
df['smoking_status'] = le.fit_transform(df['smoking_status'])


df.head()

	gender	age	hypertension	heart_disease	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
0	1	67.0	0	1	1	2	1	228.69	36.6	1	1
1	0	61.0	0	0	1	3	0	202.21	28.9	2	1
2	1	80.0	0	1	1	2	0	105.92	32.5	2	1
3	0	49.0	0	0	1	2	1	171.23	34.4	3	1
4	0	79.0	1	0	1	3	0	174.12	24.0	2	1

corr = df.corr().round(2)
plt.figure(figsize=(10,7))
sns.heatmap(corr, annot = True, cmap = 'RdYlGn');

• We can confirm that some of the variables are multicollinear based on the correlation matrix mentioned above. An example of this is the 0.68 correlation between the ever married and age columns.

Data Processing & Modeling

All the predictor variables will be mapped to an array x and the target variable to an array y. The target variable is ‘stroke’ column.

# Models
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.cluster import KMeans
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import plotly.figure_factory as ff

x=df.drop(['stroke'], axis=1).values  #Features
y=df['stroke'].values #target variable


# Evaluation
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)

# show the value counts of the calsses in the target
# we can find data impalance
print(df['stroke'].value_counts())
df['stroke'].value_counts().sort_index().plot.bar()

0    4860
1     249
Name: stroke, dtype: int64

<AxesSubplot:>

HANDLING IMBALANCED DATA

• USING SMOTE TECHNIQUE TO HANDLE THE IMBALANCED DATA.

from imblearn.over_sampling import SMOTE

print("Before OverSampling, counts of label '1': {}".format(sum(y_train==1)))
print("Before OverSampling, counts of label '0': {} \n".format(sum(y_train==0)))

sm = SMOTE(random_state=2)
x_train_res, y_train_res = sm.fit_resample(x_train, y_train.ravel())

print('After OverSampling, the shape of train_X: {}'.format(x_train_res.shape))
print('After OverSampling, the shape of train_y: {} \n'.format(y_train_res.shape))

print("After OverSampling, counts of label '1': {}".format(sum(y_train_res==1)))
print("After OverSampling, counts of label '0': {}".format(sum(y_train_res==0)))

Before OverSampling, counts of label '1': 169
Before OverSampling, counts of label '0': 3407 

After OverSampling, the shape of train_X: (6814, 10)
After OverSampling, the shape of train_y: (6814,) 

After OverSampling, counts of label '1': 3407
After OverSampling, counts of label '0': 3407

Model

def Model(model):
    model.fit(x_train,y_train)
    #Score 
    score = model.score(x_test, y_test)
    model_train_score = model.score(x_train, y_train)
    model_test_score = model.score(x_test, y_test)
    
    # Model Testing
    y_pred = model.predict(x_test)
    
    # Evaluation
    cm = confusion_matrix(y_test,y_pred)
    print(f'Accuracy Score: {accuracy_score(y_test,y_pred).round(3)}')
    
    conf_matrix = pd.DataFrame(cm,columns=['Predicted:0','Predicted:1'],index=['Actual:0','Actual:1'])
    
    z=cm
    fig = ff.create_annotated_heatmap(z, x=['Not Stroke','Stroke'], y=['Predicted Not Stroke','Predicted Stroke'],
                                      colorscale='Fall',xgap=3,ygap=3)
    fig['data'][0]['showscale'] = True

    fig.update_layout(title_text='<b>Confusion Matrix<b>',
                      title_x=0.5,
                      titlefont={'size': 15, 'family': 'Courier New'},
                      width=600, height=300,
                      xaxis_showgrid=False,
                      yaxis_showgrid=False,
                      yaxis_autorange='reversed', 
                      paper_bgcolor=None,
                      )

    fig.show()

# Decision Tree Classification
d_classif= DecisionTreeClassifier()
Model(d_classif)

Accuracy Score: 0.911

# Support Vector Machine Classification
RF_classif= SVC()
Model(RF_classif)

Accuracy Score: 0.948

# Random Forest Classification
RF_classif= RandomForestClassifier()
Model(RF_classif)

Accuracy Score: 0.948

# K Nearest Neighbour
RF_classif= KNeighborsClassifier()
Model(RF_classif)

Accuracy Score: 0.943

# Naive Bayes
RF_classif= GaussianNB()
Model(RF_classif)

Accuracy Score: 0.873

# Logistic Regression
lg_reg=LogisticRegression(max_iter=5000)
Model(lg_reg)

Accuracy Score: 0.948

• From the above accuracy scores, Logistic Regression, Random Forest Classification and Support Vector Machine models all gives high accuracy score of 0.948.

conclusions